A maximum entropy-driven support vector classification model for seismic collapse fragility curves estimation of reinforced concrete frame structures

Abstract

A novel method called maximum entropy-driven support vector classification (MED-SVC) for estimating the seismic collapse fragility curves of reinforced concrete (RC) frames is proposed in this paper. In comparison to kernel-based learning (KBL) methods, a subset selection process based on the maximum Renyi entropy criterion is embedded into the proposed MED-SVC, which can actively select an informative small-scale subset from a large-scale training dataset. The selected subset can accurately reflect the data distribution of the large-scale training dataset, which can be further used to explicitly establish a high-dimensional feature variable. Due to this, the computational complexity of training the proposed MED-SVC model only depends on the dimension of the feature variable, which can effectively solve the problem related to the low efficacy of training the KBL model in the context of a large-scale training dataset. Meanwhile, the proposed MED-SVC model can also reduce the negative effect of randomness in the subset selection process on predictive performance. The robustness, accuracy, and efficiency of the proposed MED-SVC model are thoroughly validated by comparison with random sampling driven SVC (RSD-SVC), KBL methods, random forest, neural network, and naive bayes based on 33,000 damage data of RC frames under earthquakes. Then, the proposed MED-SVC model is applied to estimate the seismic collapse fragility curve of an RC frame structure outside the aforementioned dataset. The results show that the proposed MED-SVC model can generate seismic collapse fragility curves that agree well with those produced by the finite element method (FEM) and performs more robustly than RSD-SVC.